Predictors and Correlates of Prehospital Delay Among Acute Stroke Patients in Thiruvananthapuram District, Kerala: A Cross-Sectional Survey

Introduction Stroke is a significant global public health challenge, contributing to high mortality rates and long-term disability. Beyond its physiological impact, stroke imposes a substantial socioeconomic burden on patients, families, and healthcare systems. Timely intervention, particularly through the prompt administration of reperfusion therapies, is crucial in reducing adverse outcomes. However, delays in reaching a healthcare facility after symptom onset often prevent patients from receiving these lifesaving treatments. Understanding the prevalence and factors contributing to prehospital delays is essential for improving stroke care and patient outcomes. This study aimed to assess the prevalence of prehospital delays and their correlates among stroke patients in Thiruvananthapuram district, Kerala, while also exploring the situational challenges patients face in accessing healthcare facilities. Materials and methods A hospital-based cross-sectional survey was conducted among 170 patients with confirmed acute stroke who presented to the emergency departments of four hospitals in Thiruvananthapuram district. Patients arriving within four and a half hours of symptom onset were classified as "early arrivals," while those arriving after this period were categorized as "delayed/late arrivals." Data collection was facilitated using Open Data Kit software and analyzed with IBM SPSS Statistics for Windows, Version 22.0 (Released 2013; IBM Corp., Armonk, NY, USA). Univariate and multivariate analyses were performed to identify associations. Additionally, freewheeling interviews were coded to complement and validate the quantitative findings. Results The median time from symptom onset to hospital arrival was 6.75 hours (IQR: 2.27-17.48 hours), with 40% of stroke patients experiencing prehospital delays. Bivariate analysis revealed significant associations between delay and factors such as age, housing type, income source, occupation, socioeconomic status, presence of dependents, choice of healthcare facility, number of facilities approached, and Modified Rankin Scale score. Multiple regression analysis identified housing type and the number of facilities approached as significant predictors of prehospital delay. Freewheeling interviews further indicated that, regardless of sociodemographic characteristics, hesitation, reluctance, and self-assessment were the primary reasons for delayed hospital arrival. Conclusions The study identified a high prevalence of prehospital delay (40%) among acute stroke patients in Thiruvananthapuram, the capital city of Kerala. These findings emphasize the need for health promotion strategies aimed at increasing public awareness of early stroke symptoms, ensuring the direct transfer of patients to hospitals equipped with CT scan and thrombolysis facilities, and standardizing referral processes with uniform protocols to minimize delays and improve patient outcomes.